1. Introduction
Every country in the world, except Africa, faces significant challenges due to the increasing elderly population, with China being particularly affected [1]. According to the seventh population census released in 2020, China has 260 million people aged 60 and above, including 190 million aged 65 and above. By 2030, China’s elderly population is projected to surpass that of many developed countries, ranking first according to relevant data analyses [2,3]. Nationally, the proportions of people aged 60 and 65 and over in villages are 23.81% and 17.72%, respectively, which are 7.99 and 6.61 percentage points higher than in towns and cities [4]. The urban–rural disparity in aging levels is closely related to population mobility and economic and social factors [5]. As urbanization advances, more people are moving from rural areas to cities, leading to an increase in the elderly population in large cities and a higher proportion of elderly people in rural areas. Rural areas now face “premature aging” and the emergence of “empty nest villages” [6]. Due to traditional customs and economic conditions, the elderly in rural China generally choose to spend their old age in their own homes. However, environmental pollution and construction destruction have led to the gradual deterioration of living conditions in rural areas [7,8]. Additionally, many village houses, self-built by villagers, lack scientific and reasonable planning, leading to unsupervised alterations or expansions that further deteriorate the village living environment [9,10,11]. Therefore, as the aging of China’s rural population becomes more severe, improving the quality of the rural residential environment to enhance livability and the well-being of the elderly has become a pressing issue requiring in-depth research and discussion.
Relevant aging policies indicate that age-friendly renovation enhances the rural living environment. However, survey data show that most funds for age-friendly renovation are provided by the government, and the high funding gap hinders further promotion of such renovations, necessitating active participation from all parties [12,13,14]. Some studies suggest that consumers should contribute to the funding for aging in place. Previous studies on different types of consumers’ willingness to pay (WTP) have found that socio-economic factors, such as income, education level, and occupation, impact consumers’ intentions and WTP. Hence, certain factors may positively influence people’s WTP for age-friendly retrofitting [15,16,17,18]. This study aims to understand consumers’ intentions for age-friendly retrofitting of rural dwellings, enhance public awareness of such retrofitting, and encourage active consumer participation.
However, during the promotion of age-friendly retrofitting, misunderstandings have led many to believe that such retrofitting is expensive and technologically immature, with the rural elderly population being particularly resistant to environmental changes [19,20,21,22]. In this regard, this study aims to deepen consumers’ understanding of age-friendly retrofitting and provide theoretical guidance for future rural residential design and retrofitting by calculating the associated costs and understanding the retrofitting effects and budgets. By integrating the technology acceptance model, customer perceived value theory, and the theory of planned behavior, this study analyzes consumer WTP and its influencing factors. This study found that most of the respondents were willing to undergo age-friendly retrofitting. Respondents’ education level, perceived psychological benefits, and perceived social benefits were positively associated with their WTP. In addition, high education level, high monthly personal income, and the option of aging in place had a positive impact on the remodeling budget, while increasing age, unemployment, and increased perceived situational risk had a negative impact. This will help consumers make informed decisions about age-friendly retrofitting and assist the government in understanding consumer opinions and industry perceptions to implement appropriate measures or policies.
This paper offers a new perspective on consumer concerns and attitudes towards age-friendly retrofitting by analyzing influencing factors and quantifying retrofitting costs. Section 2 reviews the current status of age-friendly retrofitting in other countries and research on rural housing and WTP. Section 3 details the methodology, including the questionnaire design, while Section 4 presents the retrofitting program and costing methodology. Section 5 discusses the results of data analysis, retrofit costing, consumer WTP, and the main influencing factors. The final section concludes with policy recommendations, suggestions for future research, and limitations of the study.
2. Literature Review
2.1. Practical Paths of Aging Adaptation at Home and Abroad
Scandinavian countries have led the way in retrofitting living environments for the elderly since the 1970s, focusing on improving comfort and convenience. Other developed Western countries have also adopted age-friendly renovation measures. For instance, the United States enacted the Older Americans Act, which outlines standards for age-friendly facilities. Germany introduced the Nursing Care Insurance Act in 1994, implementing relevant aging-friendly renovation programs. In contrast, China began addressing aging issues in the 1990s and has recently increased policy support, including revising the Law on the Protection of the Rights and Interests of the Elderly and issuing Regulations on the Construction of a Barrier-Free Environment. However, both domestic and international practices in aging adaptation lack comprehensive policies and face issues like insufficient coverage and financial constraints. Hence, accelerating the development of the age-friendly transformation industry, raising awareness among elderly families, and exploring mechanisms to enhance the industrialization of age-friendly transformation are crucial.
Overseas studies demonstrate that modifying home environments for the elderly can significantly improve their quality of life and functionality. Interprofessional interventions, such as the CAPABLE program, which combines nurses, occupational therapists, and handymen, have achieved positive results by targeting individual physical functioning within the home environment [23]. Research has also found that client-centered home modification intervention programs improve older people’s daily activities and provide a safer, more comfortable living environment [24]. Home modification guides for specific populations, such as those with visual impairments, can help individuals better adapt to their home environments [25]. Additionally, home environment modifications play a crucial role in preventing fall-related injuries among older adults. Low-cost home modifications and rehabilitation can effectively reduce fall injuries [26]. Implementing a home environment modification program can also significantly enhance the ability of older people with dementia to perform daily living tasks and reduce the psychological burden on caregivers [27].
In contrast, aging in place is the most common aging option in China, with residential buildings and communities designed to suit older people to improve their health and quality of life [28]. However, a study on the differences in the status and demand for home-based long-term care services in urban and rural China found higher demand for care in rural areas, coupled with insufficient supply compared to urban areas [29]. Additionally, research on age-friendly furniture design in Chinese home environments emphasized the importance of adapting living spaces to meet older people’s needs [30]. Renovating older neighborhoods should prioritize resident satisfaction, focusing on improving living conditions and sustainability [31]. Zhou proposed a cost allocation model to address unreasonable cost distribution in old residential neighborhood transformations, promoting cooperation among all parties to expedite the transformation process [32]. This dissertation examines the design of universally accessible public spaces in Shanghai’s Zhonghua village, emphasizing the needs of the elderly and proposing strategies to improve spatial designs based on on-site surveys and interviews [33].
While foreign studies emphasize improving the comfort and functionality of older people’s living environments, Chinese studies focus more on urban renewal and age-friendly home transformations. Additionally, foreign research shows that age-friendly retrofitting can significantly enhance older people’s quality of life and functionality and reduce unintentional injuries. Domestic studies, however, highlight the importance of urban design and public space renovations for the elderly. In summary, domestic and international research on age-friendly retrofitting has provided theoretical support and practical guidance for home care and quality of life improvements for the elderly, promoting continuous development and enhancement in the field of age-friendly retrofitting.
2.2. Domestic and International Research on Retrofitting Willingness to Pay (WTP)
In both domestic and international studies on retrofitting WTP, consumers show a willingness to invest in energy-saving measures and improved comfort. Foreign studies indicate that consumers generally value energy conservation benefits, including personal energy savings, environmental benefits, and enhanced comfort [34,35]. Similarly, domestic studies reveal that Chinese residents are also willing to invest in energy efficiency retrofits. However, the actual retrofit process faces various barriers both at home and abroad [36,37].
Foreign studies identify barriers such as insufficient policy support, low incomes making it difficult to obtain loans, and lack of cooperation from residents [38,39]. Additionally, foreign studies highlight the significant impact of policy and financial incentives on WTP for retrofitting, including subsidies, tax breaks, and expected energy prices [40,41].
Foreign research identifies potential opportunities and challenges. For instance, Israeli studies found that potential homebuyers are willing to pay for green buildings, but government financial incentives may have detrimental effects [42]. Conversely, domestic studies focus more on investment forecasting, proposing models that consider owners’ willingness to pay, providing new perspectives on investment decisions for large-scale building energy efficiency retrofits [43].
In summary, both domestic and international studies on retrofitting WTP reveal factors such as consumer willingness, policy support, and market demand. However, there are differences in focus: foreign studies emphasize policy support and financial incentives, while domestic studies concentrate on consumer willingness to pay and investment forecasting. These studies provide theoretical guidance and policy recommendations for retrofitting WTP, emphasizing the need to consider various factors holistically to promote market development.
Although research on aging retrofitting and retrofitting WTP is extensive, covering various fields and disciplines, there is a significant lack of studies on aging retrofitting WTP. Moreover, most existing research on aging retrofitting focuses on old neighborhoods or public spaces in urban areas, with limited depth in residential aging retrofitting research in rural areas or on consumers’ willingness to pay. The emphasis on research in urban areas also highlights a gap in understanding and addressing rural needs for aging retrofitting.
3. Methodology
3.1. Field Research Method
There are a total of 413 villages in the Wuhu area of Anhui Province. In this paper, taking into account the difficulty of collecting research data and the development and natural endowment of different villages, a total of 20 villages, including Jangyao Village, Chashan Village, Dayang Village, Mafang Village, Kushan Village, Daidian Village, Huayang Village, Tieta Village, Chenghai Village, Yangchong Village, Tiemen Village, Fanmazhong Village, Sanyuan Village, Chishazhong Village, Shenlang Village, Mairen Village, Hengshan Village, Hengdong Village, Xijie Village, and Zaoyuan Village, are selected as research villages for this paper. A total of 20 villages were chosen as the research villages of this paper, covering 5% of the total number of villages, which is representative of a large area of field research.
This study designs some of the questions in the subsequent questionnaire survey based on field research in the 20 study villages to understand local customs, conditions, demographics, and economic situations. Through field visits, the household structure of different rural dwellings is fully observed, and by synthesizing and organizing the field survey and mapping, the typical household structure of the study villages is extracted. This process will aid in designing and estimating costs for the follow-up questionnaire survey.
3.2. Model Construction
Residents’ WTP for age-appropriate retrofitting of rural dwellings is influenced by the owners’ subjective willingness and various external factors. It is closely related to the owners’ interests and the comfort of the living environment and requires a significant investment of time, energy, and money. This study determines WTP on the premise that owners accept the monetary and non-monetary costs associated with retrofitting. By reviewing the literature on willingness to pay, this study compiles widely used theoretical models and combines the actual research situation in this paper. It introduces technology acceptance model, customer perceived value theory, and the theory of planned behavior in consumer context [44,45,46,47].
The technology acceptance model (TAM) states that whether or not a user adopts a system is primarily driven by his or her behavioral intention, which in turn is influenced by attitude toward use and perceived value. Of these, attitude toward use is determined by a combination of perceived usefulness and ease of use. With the deepening of research, the application scope of TAM has been gradually broadened, and scholars at home and abroad have applied it to many fields such as education, architectural design, and psychology. In this paper, we explore how external variables indirectly act on older people’s willingness to age-appropriate housing retrofits by influencing their psychological factors. Specifically, older people’s willingness to retrofit will be stronger if they perceive these retrofitting measures to be both convenient and practical.
In the field of consumer behavior research, the perceived value theory occupies a pivotal position. It focuses on the perspective of consumers and analyzes their evaluation criteria and needs for products, as well as their psychological dynamics during the shopping process. In recent years, with the in-depth study of consumer behavior, the perceived value theory has been widely explored and applied, aiming to help enterprises better understand consumers and guide the formulation of their product and service strategies, so as to enhance market competitiveness and improve customer satisfaction. Based on the perceived value theory, this paper explores the factors affecting consumers’ perceived risks and perceived benefits from the perspective of the consumers of age-adapted rehabilitation, and then analyzes how these factors affect their payment decisions.
The theory of planned behavior occupies an important position in the field of social psychology and is used to explain the mechanism of generating individual behavior. Through in-depth research and empirical testing, academics have continued to refine and develop the theory, exploring the interactions between various elements and their impact on behavior. Based on this theoretical framework, the purpose of this paper is to explore the relationship between purchase intention and consumers’ willingness to invest in age-friendly renovation, and to analyze the degree of influence of related variables, with a view to identify the key factors affecting the willingness to pay for age-friendly renovation of rural residences, so as to provide effective strategies for promoting the development of age-friendly renovation for rural residences.
This paper identifies the influencing factors of residents’ willingness to pay through a literature review and constructs the “Willingness to Pay Decision Making Model for the Adaptive Retrofit of Rural Residential Houses”, as shown in Figure 1. The influencing factors are explained as shown in Table 1.
3.3. Questionnaire Design and Data Collection
This study conducted two phases of formal questionnaire research. The first phase mainly focuses on the needs of the rural elderly for aging adaptation, elaborated in the third part of the study. The second stage is divided into two parts: pre-survey and formal research. First, we extracted the representative and relevant literature in the field, including papers, reports, and case studies, etc. through databases such as WOS and Scopus, in order to review and extract the influencing factors that match and are relevant to the objectives of this paper. Secondly, the classical theories and research frameworks affecting WTP are extracted by screening relevant theories and frameworks. Finally, the core influencing factors are summarized, summarized and established, and the core influencing factors suitable for use in this study are summarized through an in-depth review of the literature in order to design the questionnaire for the second stage. The pre-survey involved electronic questionnaires and interviews in 20 research villages, randomly selecting potential consumers aged 45 years or older to conduct a small survey. A total of 50 questionnaires were collected. The results of the pre-survey were analyzed to validate the reasonableness of the numerical intervals of the questionnaires, supplement some of the questions and options, and modify those that are ambiguous or difficult to understand. The formal research questionnaire in the second stage is divided into three parts: (i) introduction of the design scheme and costing results (see Section 3 for details); (ii) respondents’ personal information and socio-economic attributes (e.g., gender and age, education level, occupational income, physical condition, etc.); and (iii) respondents’ WTP for aging-friendly retrofitting and their influencing factors [48,49,50,51,52,53,54,55,56,57,58,59]. The questions are combined with a decision-making model design, involving respondents’ perceived need for age-friendly retrofitting, perceived benefits and risks, and willingness to invest in retrofitting, and presented in Table 2.
There are about 20,000 middle-aged and elderly people in the 20 study villages, the target sample size for this study was 500, and 451 completed questionnaires were received, of which 430 met the established requirements. The final number of questionnaires collected was 2.15% of the total, which is acceptable and equally representative when collecting questionnaires for a large group of people. Test statistics indicated high reliability and strong data correlation, making the dataset suitable for further factor analysis. Data were collected using an online structured questionnaire via the Questionnaire Star platform. Respondents, randomly selected from 20 villages in the study area, were evenly distributed across these villages. All respondents were aged 45 years and above, ensuring a reasonable structural distribution and thereby maintaining scientific rigor in data collection.
4. Retrofit Program and Costing
4.1. Extraction of Typical House Types in the Area
Through field visits to the study villages and interviews with rural residents, the dwelling types of the 20 villages were categorized into three types. The modern multiple parallel three-dimensional layout dwellings is the most prevalent. This type is convenient for production and living, with reasonable internal functional zoning. The central room is centrally located on the ground floor, used for receiving guests, dining, and relaxation. Bedrooms and storage spaces for the elderly are usually situated on both sides of the first-floor central room. Staircases to the first floor are typically at the back of the central room, with washrooms, bathrooms, or storerooms often located beneath the stairs to save space. Kitchens are typically positioned in the corners of the ground floor. The first floor above the central room usually includes a living room for reception and rest, adjacent to the main secondary bedroom. This vertical division of space allows for the independence of functional rooms while maintaining communication between them via stairwells, making it ideal for larger families.
Traditional one-story tandem layout houses, due to long construction periods, changes in family structure, and poor living environments, are becoming obsolete and are of little research significance. Similarly, rural houses characterized by high costs, high energy consumption, and low utilization rates are not the focus of this study. To ensure the typicality and timeliness of rural residential exploration, the research will concentrate on the modern multiple parallel three-dimensional layout of residential types, refining the building type to establish a basic prototype of this residential layout, and presented in Figure 2.
4.2. Research on Behavioral Patterns and Modification Needs of the Elderly
4.2.1. Research on Behavioral Patterns of the Elderly
The production and lifestyle of the rural elderly are greatly influenced by their age and unique agricultural behavior. This study considers various demographic factors of the elderly sample, including gender, age, and village, to minimize the bias of results from any single data point. To ensure a wide range of situations, strong representation, and extensive coverage in the elderly sample selection, this study employs a random number algorithm, a scientific and objective method, to select 20 different subjects. Twenty different research subjects were scientifically and objectively sampled, and through interviews, the proportion of time the elderly spend in different indoor areas during their daily activities was understood. Table 3 shows that the bedroom and the central room are the areas where the elderly spend the most time indoors, thus presenting a higher probability of danger. Therefore, the bedroom and the central room are prioritized as key areas for rural residential aging retrofit.
4.2.2. Elderly Adaptation Needs Study
In this paper, a total of 160 questionnaires were distributed to the elderly across three age groups in the twenty study villages. Of these, 150 questionnaires were collected, resulting in a return rate of 93.75%, which meets the study’s requirements. The questionnaires were designed using a Likert scale to understand the retrofitting needs of the rural elderly from two perspectives: internal residential areas and age-appropriate retrofitting projects. The internal areas of the residence include the central room, bedroom, kitchen, washroom, bathroom, and storeroom, and presented in Figure 3.
With reference to the National Office for the Aging’s *Guidance on Accelerating the Implementation of Aging Adaptation Retrofit Projects for the Elderly at Home* and Wuhu City’s *Implementation Program for Accelerating the Implementation of Aging Adaptation Retrofit Projects for the Elderly at Home*, and considering the actual conditions of the study villages and the focus of this paper, sixteen retrofit projects were identified. These projects include anti-slip treatments, installation of handrails, modification of door handles, installation of flashing vibrating doorbells, configuration of anti-pressure sore mats, conversion of squatting washrooms to seated washrooms, faucet modifications, bathtub/shower room modifications, configuration of shower chairs, installation of automatic sensing lamps, modification of power sockets and switches, installation of protective strips, anti-collision corners, configuration of aging-adapted furniture, and provision of geriatric products. The rural seniors reflected their retrofitting needs by scoring these areas and projects on the Likert scale.
Based on the Likert scale scores, significant differences were observed in the demand for retrofitting different areas within the residence and for various aging-friendly retrofitting projects. The highest demand was for retrofitting washrooms and bathrooms, followed by bedrooms and kitchens. The demand for retrofitting central rooms and storerooms was notably lower. Elderly participants showed a strong interest in projects such as anti-slip floor treatments, addition of handrails, conversion of squatting washrooms to bidets, configuration of aging-friendly furniture, and provision of geriatric products. Conversely, they showed less interest in the modification of downward-pressing door handles and configuration of anti-pressure sore pads, and presented in Figure 4.
4.3. Retrofit Program and Cost Estimates
4.3.1. Retrofit Program
Based on the findings above, the objective need for retrofitting bedrooms and central room is higher, and the demand for retrofitting kitchens, bathrooms, and washrooms is also significant. Therefore, bedrooms, central rooms, kitchens, bathrooms, and washrooms are identified as the key areas for retrofitting the interiors of rural dwellings. This study classifies the relevant aging retrofit projects into five categories: building hardware retrofit, furniture and home decoration retrofit, elderly aids, intelligent appliances, and universal design. Based on the compilation of literature and policy research on aging retrofit in rural residential interiors, combined with the study of the demand of the elderly in villages for different aging retrofit projects, a design scheme for aging retrofit in rural residential interiors is formulated [60,61,62,63,64,65,66,67,68], and presented in Table 4.
4.3.2. Cost Estimates
This study divides the indoor aging renovation of rural houses into two categories: hard and soft. The hard category includes projects under building hardware renovation, while the soft category encompasses furniture and home decoration renovation, senior aids, intelligent appliances, and indoor universal design. The prices for the hard furnishing renovation materials are sourced from the Costcom website. Using the market price column, the latest prices of building materials in Anhui Province as of January 2024 are collected and collated, and the average value of the quotations from each supplier on the website is used to determine the cost of the selected building materials. The prices for the soft furnishing transformation projects are sourced from Taobao and Jingdong, two major online business platforms. The average prices of such goods are taken from the flagship stores of each brand on these platforms. The principle for selecting each commodity is to choose the style with the lower price while ensuring quality, adhering to the principle of cost-effectiveness, and presented in Table 5.
The total cost of the final indoor aging renovation of rural houses is CNY 12,224.47, with the hard-fitting part costing CNY 10,230.47 and the soft-fitting part costing CNY 1994. This indicates that the primary expense in rural aging renovations lies in the hard-fitting segment, particularly in the treatment of non-slip flooring. However, considering that some families have already incorporated relevant aging factors in their housing construction, such as non-slip floor tiles, bedroom flooring, and the installation of sound insulation and heat preservation doors and windows, the distinction between hard and soft furnishing costs can be targeted at different groups of people. This differentiation will inform the subsequent willingness to pay questionnaire design.
5. Results and Discussion
5.1. Descriptive Statistics
5.1.1. Demographic Characteristics of Respondents
This study distributed questionnaires to the 20 study villages, collecting a minimum of 20 responses per village with a maximum deviation of six questionnaires. This suggests the questionnaires were collected under favorable conditions with good coverage. The male-to-female ratio of respondents is similar, with slightly more females than males. The highest number of respondents fall within the middle-aged group (45–60 years old), followed by older individuals aged 60–90 years old, reflecting the rural population’s age distribution. Most respondents have an educational background of less than high school, which is typical for rural areas. The respondents’ monthly personal income is primarily less than CNY 5000 due to some elderly being retired without pension insurance, thus lacking current income sources. The majority of respondents are retired, consistent with the occupational status of elderly individuals in rural areas. Most respondents have lived in their current residences for more than 5 years; shorter durations may be due to recent rural beautification efforts leading some residents to rebuild or renovate their original plots. Most respondents live with elderly family members, typically one or two individuals, in line with regional customs where elderly parents often live with their adult children, resulting in common three-generation households. Rural residents primarily prefer home care in their old age, reflecting the traditional Chinese emphasis on family. Few respondents are completely unable to care for themselves, and a small proportion require partial assistance, with the majority being fully independent. Overall, the sample survey conforms to social norms and current conditions, and generally meets the criteria of random sampling, and presented in Table 6.
5.1.2. Willingness to Pay
Table 7 and Table 8 on respondents’ willingness to retrofit and pay show that over 80% are willing to retrofit their homes, 60% are willing to pay, and 20% have cost considerations, adopting a wait-and-see approach or committing only a small portion due to income constraints and practical concerns. Research findings on payment amounts indicate a gradual decrease with increased capital investment, with most respondents willing to pay either less than CNY 1000 or between CNY 1000 and CNY 3000. This trend is largely due to the rural location of the study area and its focus on middle-aged and elderly individuals over 45 years old, who typically have lower incomes. Overall, households exhibit a limited willingness to pay for rural house aging transformation, although significant interest persists.
The formula for calculating the average willingness to pay (WTP) for aging rural dwellings is as follows:
(1)
where θi represents the willingness to pay price, Pi denotes the frequency of each corresponding price, and n represents the total number of bids for willingness to pay prices, which in this study is n = 4.Since some respondents expressed unwillingness to pay in the second-stage questionnaire of this study, direct exclusion would not accurately reflect their responses’ authenticity. Therefore, this study employs the Spike model to adjust the willingness to pay, using the following adjusted formula:
(2)
Since the questionnaire uses price intervals for willingness to pay, each interval is defined here by its median value for ease of calculation. Specifically, unwillingness to pay is set at CNY 0, the interval less than CNY 1000 is defined as CNY 500, CNY 5000 or more as CNY 6000, CNY 1000–3000 as CNY 2000, and CNY 3000–5000 as CNY 4000. The mean value of positive willingness to pay, , was calculated as CNY 2384.85, and the mean value of non-negative willingness to pay, , was CNY 2024.74 (Figure 5 and Figure 6).
Combined with the distribution of positive willingness to pay and the cumulative frequency distribution of non-negative willingness to pay, the median willingness to pay falls within the range of CNY 1000 to CNY 3000, closely aligning with the calculated average willingness to pay, and is thus considered reasonable.
5.2. Analysis of WTP Influencing Factors for Aging Adaptation in Rural Housing
Since the questionnaire data needs to be analyzed using SPSS 28 software, it is essential to digitize the responses to the questionnaire items, specifically defining the variables that may influence the outcomes. The detailed definitions of these variables are presented in the Table 9. In addition, in order to facilitate the presentation of the results of the subsequent data analysis, each variable has a unique label, such as F1, F2, F3, etc.
5.2.1. Pearson Correlation Analysis
Table 10 and Table 11 display the Pearson correlation coefficients among the primary factors influencing households’ willingness to retrofit, willingness to pay, and retrofit budget for aging retrofitting of rural dwellings. The findings indicate, at the 5% significance level, that willingness to retrofit correlates positively with retirement area selection, perceived effectiveness benefits, and social benefits perception. Willingness to pay is positively correlated with education level and retirement area selection, while negatively correlated with age group and perceived ease of use. Retrofit budget is positively correlated with subjective norms and negatively correlated with age group and perceived ease of use. Additionally, at the 1% significance level, retrofitting budget shows positive associations with education and retirement area selection, and negative associations with age group, employment status, and length of residence.
These findings underscore the significance of retirement area selection, education level, and age group in shaping households’ attitudes toward retrofitting, willingness to pay, and retrofit budgets for aging retrofit in rural residences. Notably, retirement area selection emerges as the most influential factor, suggesting that households opting to age in place are more likely to pursue residential aging retrofitting. This trend aligns with the prevalent preference among Chinese elderly for aging within familiar home environments, reflecting societal values that prioritize honoring elderly preferences and fostering family-based care.
Moreover, education level significantly influences willingness to pay and retrofitting budgets. Those with higher education levels demonstrate greater willingness and capacity to invest in aging-friendly retrofitting. This tendency likely stems from their enhanced capacity to grasp and embrace novel concepts, heightened awareness of the importance of retrofitting for the elderly, and greater financial resources to support such initiatives.
Conversely, the negative correlation between age group and willingness to pay and retrofitting budget may indicate apprehension among some older individuals regarding retrofitting projects. They may fear potential disruptions to their lifestyles or perceive retrofitting costs as prohibitive, thereby exhibiting lower willingness to pay and allocating smaller retrofitting budgets.
In addition to this, there is a certain degree of a significant relationship between age and the level of education, employment, personal monthly income, length of residence, the number of elderly people living with them, the choice of retirement area, and physical condition, which makes the age group the most complex influencing factor in the process of aging rehabilitation. Additionally, the level of education decreases with the increase in age due to the fact that education was not yet popularized in China in the early years. The fact that retirement or unemployment also results in a decrease in personal monthly income further aggravates economic pressure, which may lead to a decrease in willingness to pay. Along with the gradual deterioration of the physical condition and the gradual increase in residence time, there is a deeper emotional attachment to the existing housing, which makes them more inclined to aging in place, which may lead to an increase in their willingness to pay. All these factors, centered on the age group, together constitute a complex network that influences the willingness to pay for aging rehabilitation of rural residents. Age groups also affects the willingness to pay in a complex network of interactions. Not coincidentally, subjective norms likewise play the same role in influencing respondents’ perceived benefits and perceived risks, suggesting that the opinions and suggestions of others and the atmosphere of society have a subtle influence on consumers.
5.2.2. One-Way Analysis of Variance (ANOVA)
Table 12, Table 13 and Table 14 present the results from one-way ANOVA analyses investigating willingness to retrofit, willingness to pay, and retrofit budget. The results showed some trends consistent with the correlation analysis. First, subjective norms, perceived effectiveness benefits, and perceived social benefits had a more significant degree of influence on respondents’ willingness to retrofit. This suggests that individuals’ internal normative perceptions and perceptions of efficacy and social benefits from rehabilitation have an important influence on willingness to rehabilitate, which is consistent with the results of previous correlation analyses, and once again confirms the importance of individual values and social perceptions on behavioral decision-making. Second, in terms of willingness to pay, education level, perceived psychological benefits, and perceived social benefits have a significant effect on respondents’ willingness to pay. This is in line with the results of the correlation analysis, and further confirms that higher levels of education and higher levels of perceived psychological and social benefits motivate individuals to show a higher willingness to pay. This may be due to the fact that individuals with higher levels of education are better able to understand the importance of rehabilitation, and the greater the perceived psychological and social benefits, the greater the individual’s support for the rehabilitation program. Finally, in terms of remodeling budget, age group, education level, employment status, individual monthly income, choice of retirement area, and perceived situational risk had significant effects on respondents’ remodeling budget. Among them, education level, personal monthly income, and retirement area choice show a positive correlation, while age group, employment situation, and perceived situational risk show a negative correlation. This suggests that an individual’s economic and social status, level of monthly income, and perceived level of future risk affect the extent to which they develop and support a retrofit budget. This finding is consistent with the results of previous correlation analyses and re-emphasizes the important influence of an individual’s social background and economic conditions on retrofit budgets.
5.2.3. Scale Mean Analysis
The 430 questionnaires collected were finally counted as shown in Table 15, and for dimensions with multiple questions, the average score was computed. The mean scores of each variable, in descending order, are as follows: ① perceived psychological benefits (4.56) > ② perceived effectiveness benefits (4.555) > ③ perceived social benefits (4.535) > ④ perceived price (attitude toward behavior) (4.50) = ⑤ perceived usefulness (4.50) > ⑥ perceived economic risk (4.17) > ⑦ subjective norm (4.08) > ⑧ perceived ease of use (4.05) > ⑨ use of attitudes (perceived behavioral control) (4.01) > ⑩ perceived technological risk (3.94) > ⑪ perceived situational risk (3.92). This ranking provides insight into the respondents’ perceptions across various dimensions related to aging retrofit in rural dwellings.
The majority of respondents in the sample expressed a willingness to conduct aging retrofitting in their homes. After ranking the mean scores of the eleven influencing factors, households in the study villages demonstrated a strong acceptance of perceived benefits and usefulness, highlighting their focus on the personal advantages of retrofitting. This finding is consistent with the theoretical framework and the willingness to pay model proposed in this study. Presenting the preliminary results during the questionnaire survey effectively aided respondents’ understanding of aging retrofitting goals and implications. As societal development improves living conditions, rural residents increasingly prioritize their living environment, showing readiness to invest economically in enhancements. Despite acknowledging perceived risks, respondents believe in the potential benefits and their capability to manage renovation challenges. A high recognition of subjective behavioral norms indicates that government policies, social networks, and peer influence significantly influence retrofitting decisions. However, uncertainty about potential barriers and decision-making difficulties contributes to lower scores for perceived behavioral control.
5.2.4. Characterization of High WTP Populations
In this study, we classify the group with higher willingness to retrofit, higher willingness to pay, and a retrofit budget exceeding CNY 5000 as the high willingness to pay group, comprising 48 eligible samples out of 430. The characteristics of this group are distinctive, as shown in Table 16. Firstly, over three-quarters of respondents have personal monthly incomes exceeding CNY 2500, with nearly half earning CNY 5000 or more, indicating their robust financial capability to cover aging-friendly retrofitting costs. Secondly, a significant feature is their higher educational attainment, as approximately 68% have completed high school or higher, suggesting a heightened awareness of the importance of enhancing their living environment. Additionally, a majority choose to age in place, aligning with modern societal trends and reflecting their strong intrinsic motivation for aging-friendly renovations. Finally, the high willingness to pay group predominantly falls within the 45 to 60 age bracket, with over half currently employed, indicating stable current lifestyles and heightened expectations for future quality of life. These characteristics collectively illustrate the group’s pursuit of a comfortable, secure, and autonomous lifestyle in contemporary society.
6. Conclusions
In this study, we investigate the willingness to pay and the factors influencing it for the aging retrofitting of rural houses in Wuhu, Anhui Province from a consumer perspective. A theoretical model of consumer retrofitting decision-making is constructed by integrating methods such as literature analysis, field research, and questionnaire surveys. Simultaneously, we develop a house retrofitting scheme and estimate the associated costs, aiming to provide consumers with a clearer understanding of the potential effectiveness of retrofitting prior to their decision-making. Lastly, a questionnaire survey was conducted among middle-aged and elderly individuals aged 45 and above in twenty villages in Wuhu to identify the primary factors influencing households’ readiness to invest in aging-friendly retrofitting. The main findings and conclusions are as follows:
1. The household structure in the study area is classified into three categories: traditional one-story tandem layout, modern multiple parallel three-dimensional layout, and rural houses. The modern multiple parallel three-dimensional layout is identified as the typical rural household structure in this study, based on findings from field research visits. Simultaneously, through telephone interviews, we aimed to understand the behavioral patterns of the elderly and gain insights into the critical areas for rural residential aging renovation at an objective level. Secondly, we collected 150 questionnaires from rural elderly individuals, supplemented by relevant policy documents, to assess their requirements across various areas and measures of residential retrofitting. Subsequently, we identified bedrooms, central rooms, kitchens, washrooms, and bathrooms as the primary areas for rural residential aging retrofitting. The aging transformation projects encompass five aspects: building hardware transformation, furniture and home decoration transformation, senior aids, intelligent appliances, and universal design. These projects were formulated based on the questionnaire data. The transformation cost was estimated at CNY 12,224.47 by applying this program to typical household structures and considering construction material prices from the Costcom website and the average appliance prices on major e-commerce platforms. Of this total, CNY 10,230.47 is allocated to hard decoration and CNY 1994 to soft decoration.
2. The questionnaire survey yielded 430 valid responses, demonstrating good reliability and validity. More than 80% of respondents expressed willingness to undergo residential aging transformation, with 60% indicating readiness to invest in such retrofitting. The distribution of investment amounts showed a gradual decrease with higher amounts, with a concentration around CNY 3000. Overall, residents in the research area showed a strong inclination towards aging retrofitting. The average willingness to pay for aging-friendly renovation was estimated at CNY 2024.74 for rural residences in Wuhu, Anhui Province.
3. In this study, Pearson correlation analysis and one-way ANOVA were employed to comprehensively analyze the influencing factors. The results indicate significant positive correlations at the 5% significance level between willingness to retrofit and the retirement area selection, perceived effectiveness benefits, and perceived social benefits. Willingness to pay shows positive correlations with education level and the choice of retirement area, while negatively correlating with age group and perceived ease of use. Moreover, budget correlates positively with subjective norms. At the stricter 1% significance level, retrofit budget is positively associated with education level and retirement area selection, but negatively with age group, employment status, and length of residence. The study highlights the profound influence of retirement area selection and age group on rural residential aging retrofit, with retirement area choice exerting the greatest impact. Residents in the study villages demonstrate a strong inclination towards aging retrofit, emphasizing perceived benefits and usefulness, consistent with theoretical and model-based frameworks. The preliminary survey results are presented to enhance respondents’ comprehension of aging retrofit details, aligned with evolving societal expectations for enhanced living conditions. Despite recognizing risks, respondents perceive significant benefits and are confident in managing associated challenges. The recognition of subjective norms underscores the impact of government policies and social networks, though uncertainties about decision-making barriers and future challenges contribute to lower perceived behavioral control scores.
7. Suggestions
While China’s residential aging transformation started later compared to developed countries, recent advancements in policy, legislation, and financial investments have significantly promoted its development. However, rural areas have not received adequate attention in aging transformation compared to urban districts. Hence, the government should tailor transformation systems and policies to suit rural residential characteristics and intensify efforts to foster rural residential aging transformation.
1. Tailored transformation programs are essential as consumers of varying ages and backgrounds have diverse needs for aging transformation. Governments or enterprises can introduce graded transformation programs to cater to these diverse needs. Programs targeting middle-aged and elderly groups may emphasize comfort and convenience, whereas those for the elderly should prioritize safety and accessibility.
2. Differential financial support is crucial, as research indicates that some older individuals keen on retrofitting may have limited willingness to pay due to lower incomes. Thus, particularly in rural areas with lower per capita incomes, the government should implement differentiated financial support policies tailored to residents’ income levels. Low-income families could benefit from increased subsidies and loan assistance to alleviate economic burdens associated with retrofitting participation.
3. Emphasizing model villages for aging-friendly retrofitting is crucial. Respondents in the study area recognize the benefits, prompting the government to select representative rural villages for comprehensive aging-friendly retrofitting. By providing government funding and technical support, these villages can serve as models and be promoted to neighboring areas. Publicizing case studies and economic analyses will demonstrate enhanced quality of life and increased property values post-retrofitting, encouraging broader participation. This approach showcases retrofitting effectiveness through practical examples, thereby enhancing community engagement.
4. Enhanced publicity and guidance are essential. External advice and societal encouragement facilitate informed consumer decisions. The government should conduct training courses on aging-friendly retrofitting to raise rural residents’ awareness and motivation. Moreover, comprehensive publicity efforts through various channels are necessary to cultivate a supportive societal environment. Utilizing media, community activities, and other platforms, the government should emphasize the importance and benefits of retrofitting, thereby fostering community engagement.
8. Shortcomings and Prospects
While this study integrated field research results with the selection of typical household types and real case scenarios to estimate retrofitting costs, variations in household types across villages and within the same village, and fluctuations in material prices and procurement methods, introduce inherent errors and limitations into the cost estimates of retrofitting. Similarly, we have neglected the fact that mathematical models and informational tools can also be applied in this study.
Given the aforementioned limitations, future research can focus on the following areas for improvement and optimization:
1. In the process of model construction and scale question design, the theoretical foundations were further strengthened and iterative adjustments and refinements were made based on the results of the study to improve the accuracy of the content. For example, mathematical models were applied to the screening of scale questions and influencing factors, or multifactor models were used and data were analyzed with different metrics.
2. Building on this study’s conclusions, data collection on residential retrofitting to enhance accuracy in cost estimation can be further expanded and real-life experiences from retrofitted homes to optimize retrofitting design schemes can be collected.
Conceptualization, C.Y. and H.L.; methodology, C.Y. and H.L.; validation: H.L., S.Y. and X.L.; formal analysis, C.Y. and H.L.; investigation, C.Y. and H.L.; writing—original draft preparation, C.Y. and H.L.; writing—review and editing, C.Y., H.L., S.Y. and X.L.; visualization, S.Y. and X.L.; supervision, H.L. and S.Y.; project administration, H.L. and S.Y.; funding acquisition, H.L. and S.Y. All authors have read and agreed to the published version of the manuscript.
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Willingness to pay decision-making model for the adaptive retrofit of rural residential houses.
Enlarge this image.Figure 2. Modern style multi-parallel three-dimensional layout residential floor plan.
Interpretation of theoretical model variables.
| Variable | Variable Explanation | Theory | |
|---|---|---|---|
| Willingness to Pay Decision-Making Model for the Adaptive Retrofit of Rural Residential Houses | Perceived Usefulness | Residents’ perception of difficulties or obstacles in adapting rural housing for the elderly | Technology Acceptance Model |
| Perceived Ease of Use | Residents’ perception of whether adapting rural housing for the elderly will bring positive benefits to themselves | ||
| Usage Attitude | The attitude towards the willingness to pay on a psychological subjective level, derived from the combined perceptions of ease of use and usefulness | ||
| Perceived Benefit | Perceived benefits are subdivided into perceived effectiveness benefits, perceived psychological benefits, and perceived social benefits. The result of perceived benefits is evaluated through the residents’ overall views on these three aspects | Customer Perceived Value Theory | |
| Perceived Risk | Perceived risks are subdivided into perceived economic risks, perceived technical risks, and perceived situational risks. The result of perceived risks is evaluated through the residents’ overall views on these three aspects | ||
| Perceived Price | Perceived value is the residents’ subjective weighing and evaluation after considering the perceived benefits and risks | ||
| Personal Factors and Social Background | Factors such as residents’ age, gender, physical condition, and occupational status will influence their behavioral attitudes, subjective norms, and perceived behavioral control, ultimately affecting their willingness to pay and behavior | Theory of Planned Behavior in Consumer Context | |
| Perceived Behavioral Control | Residents’ perception of the difficulty of aging-friendly renovations for rural housing | ||
| Subjective Norm | The influence of others or society on residents’ decision-making or process of aging-friendly renovations | ||
| Attitude toward Behavior | People tend to make decisions based on external factors and their own evaluations of things |
Questionnaire scale question design.
| Variable | Item | Reference |
|---|---|---|
| Perceived Usefulness | I think remodeling is the future. | Wang, T [ |
| I think remodeling can be beneficial to non-elderly residents as well | Zhang, L [ | |
| Perceived Ease of Use | I think the remodeling will be somewhat hindered | Zhang, L [ |
| Use of attitudes | I think remodeling is more difficult | Gu, S [ |
| Subjective Norm | I believe that the advice of others influences the decisions I make | Lim, Y [ |
| I believe that government policy influences my decision-making | Gu, S [ | |
| Perceived Price | I think there is value in making modifications | Ketabi, S.N [ |
| Perceived effectiveness benefits | I think habitat is better after the renovation | Lim, Y [ |
| I think it is safer and more comfortable for seniors to live after remodeling | Bai, X [ | |
| Perceived Psychological benefits | I think remodeling is good for older people’s mental health | Tan, W [ |
| Perceived social benefits | I think the remodeling is conducive to a good social climate | Mahadeo, J.D [ |
| I think remodeling is the embodiment of filial piety to elders | Tan, W [ | |
| Perceived economic risk | I think the economic cost of retrofitting is higher | Bai, X [ |
| Perceived technological risk | I do not think the remodeling technology is mature enough | Wang, T [ |
| Perceived situational risk | I do not think the remodeling is meeting expectations | Rodríguez-Ortega, T [ |
Classification of daily behaviors of the elderly and time share.
| Behavioral | Behavioral | Action | Place | Percentage | Overall |
|---|---|---|---|---|---|
| Acts of necessity | break | sleeping, napping, recuperating | bedroom | 43.01% | 52.08% |
| cleanse | washing, going to the bathroom | washroom, bathroom | 3.87% | ||
| catering | dining, tea | kitchen, central room | 5.21% | ||
| Acts of productive labor | cooking | cooking | kitchen | 2.38% | 22.92% |
| domestic work | sweeping, laundry | interior | 5.95% | ||
| farming | cultivation, breeding | outdoors | 7.44% | ||
| working | travel to work | outdoors | 7.14% | ||
| Leisure and recreational behavior | diversion | watching TV, reading, surfing the web | bedroom, central room | 8.33% | 9.23% |
| exercise | morning exercise, square dance | outdoors | 0.89% | ||
| Social communication behavior | indoor Socialization | chess, parlor | central room | 6.40% | 15.77% |
| outdoor socializing | walking, shopping | outdoors | 9.38% |
Design solutions for aging-friendly retrofitting of residential interiors.
| Building Hardware Retrofit | Furniture Home | Geriatric Aids | Smart Device | General Interior Design | |
|---|---|---|---|---|---|
| Central room | Laying of non-slip floor tiles | Elderly chair | Installation of flashing vibrating doorbells | Installation of anti-collision corner guards and strips, reminder signs, power plugs, and switches retrofit | |
| Bedroom | Laying of non-slip floor tiles | Addition of handrails at bedside | Assisted dresser lifting hanger | Intelligent sensor lamps, emergency alarms | |
| Kitchen | Laying of non-slip floor tiles | Sensor faucet | Self-service eating utensils | Gas leak alarm, cookware anti-dry burning system | |
| Bathroom | Laying of non-slip floor tiles | Wall safety grab bar, bathroom heater | Placement of bathtubs with anti-slip covers | Emergency alarms | |
| Washroom | Laying of non-slip floor tiles | Wall safety grab bar, sensor faucet installation in washroom | Configuring a washroom booster | Intelligent sensor lamps, emergency alarms |
Prices of materials for age-appropriate retrofitting of residential interiors.
| Material | Price of Item | Unit | Quantities | Prices |
|---|---|---|---|---|
| Non-slip floor tiles 800 (parsonage) | 198 | m2 | 24 | 4752 |
| Non-slip floor tile 300 (washroom, bathroom) | 145.2 | m2 | 9 | 1306.8 |
| Non-slip floor tile 600 (kitchen) | 114.51 | m2 | 9 | 1030.59 |
| Solid wood laminate flooring (bedroom) | 76.84 | m2 | 12 | 922.08 |
| Insulated soundproof doors | 750 | \ | 1 | 750 |
| Thermal and acoustic windows | 734.5 | \ | 2 | 1469 |
| Barrier-free handrails (bathrooms) | 32 | \ | 1 | 32 |
| Barrier-free handrails (washrooms) | 168 | \ | 1 | 168 |
| Bedside rails | 77 | \ | 1 | 77 |
| Sensor faucet | 49 | \ | 2 | 98 |
| Heater | 158 | \ | 1 | 158 |
| Pedestal-type WC | 348 | \ | 1 | 348 |
| Elderly chair | 159 | \ | 2 | 318 |
| Dressing aid | 32 | \ | 1 | 32 |
| Lifting hanger | 16 | \ | 1 | 16 |
| Self-service meal dispenser | 48 | \ | 2 | 96 |
| Shower mat | 35 | \ | 1 | 35 |
| Shower bench | 148 | \ | 1 | 148 |
| Washroom bowl booster | 63 | \ | 1 | 63 |
| Flashing vibrating doorbell | 39 | \ | 1 | 39 |
| Intelligent sensor lamps and lanterns | 29 | \ | 2 | 58 |
| Emergency alarms | 30 | \ | 3 | 90 |
| Gas leakage alarm | 40 | \ | 1 | 40 |
| Cookware anti-drying system | 118 | \ | 1 | 118 |
| Anti-collision corner guard | 10 | \ | 1 | 10 |
| Switchgear | 10 | \ | 5 | 50 |
| total price | 12,224.47 |
Personal information collection and economic attribution survey.
| Investigative Projects | Disaggregated Indicators | Sample Size | Percentage |
|---|---|---|---|
| Research villages | Gangyao village | 21 | 4.88% |
| Chashan village | 24 | 5.58% | |
| Dayang village | 26 | 6.05% | |
| Machang village | 22 | 5.12% | |
| Kushan village | 23 | 5.35% | |
| Daidian village | 21 | 4.88% | |
| Huayang village | 20 | 4.65% | |
| Tieta village | 21 | 4.88% | |
| Chengxi village | 21 | 4.88% | |
| Yangchong village | 20 | 4.65% | |
| Tiemeng village | 20 | 4.65% | |
| Fanma village | 21 | 4.88% | |
| Saanyuan village | 20 | 4.65% | |
| Chisha village | 21 | 4.88% | |
| Shennong village | 20 | 4.65% | |
| Maren village | 20 | 4.65% | |
| Henshan village | 22 | 5.12% | |
| Hendong village | 25 | 5.81% | |
| Xijie village | 21 | 4.88% | |
| Zaoyuan village | 21 | 4.88% | |
| Genders | Male | 202 | 46.98% |
| Female | 228 | 53.02% | |
| Age | 45~60 years | 170 | 39.53% |
| 60~75 years | 123 | 28.60% | |
| 75–90 years | 102 | 23.72% | |
| 90 years and over | 35 | 8.14% | |
| Educational attainment | Not | 69 | 16.05% |
| Elementary school | 162 | 37.67% | |
| Junior high school | 66 | 15.35% | |
| Senior high school | 69 | 16.05% | |
| Junior college | 50 | 11.63% | |
| Undergraduate and above | 14 | 3.26% | |
| Employment | Incumbency | 99 | 23.02% |
| Profession | 108 | 25.12% | |
| Laid off or unemployed | 33 | 7.67% | |
| Retirement | 190 | 44.19% | |
| Monthly personal income | Less than CNY 1000 | 105 | 24.42% |
| CNY 1000~2500 | 120 | 27.91% | |
| CNY 2500~5000 | 139 | 32.33% | |
| More than CNY 5000 | 39 | 9.07% | |
| No source of income | 27 | 6.28% | |
| Length of residence | Less than 5 years | 40 | 9.30% |
| 5~10 years | 128 | 29.77% | |
| 10–20 years | 114 | 26.51% | |
| More than 20 years | 148 | 34.42% | |
| Number of elderly people | 0 | 90 | 20.93% |
| 1 | 182 | 42.33% | |
| 2 | 108 | 25.12% | |
| 2 or more | 50 | 11.63% | |
| Retirement area selection | Living at home | 377 | 87.67% |
| Community care | 31 | 7.21% | |
| Care organization | 22 | 5.12% | |
| Condition | Fully self-managed | 285 | 66.28% |
| Partially self-care | 130 | 30.23% | |
| Completely incapable of self-care | 15 | 3.49% |
Distribution of willingness to retrofit, willingness to pay, and retrofit budgets.
| Level of Willingness | Effective Sample Size | Percentage (%) |
|---|---|---|
| Willingness to retrofit | ||
| reluctantly | 2 | 2.09 |
| unwilling | 10 | 2.33 |
| general willingness | 62 | 14.42 |
| more willing | 112 | 26.05 |
| fully prepared | 237 | 55.12 |
| Willingness to pay | ||
| reluctantly | 47 | 10.93 |
| unwilling | 32 | 7.44 |
| general willingness | 81 | 18.84 |
| more willing | 92 | 21.4 |
| fully prepared | 178 | 41.4 |
| Retrofit budget | ||
| unwillingness to pay | 65 | 15.1% |
| less than CNY 1000 | 125 | 29.1% |
| CNY 1000~3000 | 124 | 28.8% |
| CNY 3000~5000 | 68 | 15.8% |
| more than CNY 5000 | 48 | 11.20% |
Frequency of willingness to pay for aging-friendly adaptations.
| Willingness to Pay Price Range | Frequency | Frequency of Positive WTP | Cumulative | Frequency of | Cumulative Frequency of Non-Negative WTP |
|---|---|---|---|---|---|
| unwillingness to pay | 65 | - | - | 15.1% | 15.1% |
| less than CNY 1000 | 125 | 34.25% | 34.25% | 29.1% | 44.2% |
| CNY 1000~3000 | 124 | 33.97% | 68.22% | 28.8% | 73.0% |
| CNY 3000~5000 | 68 | 18.63% | 86.85% | 15.8% | 88.8% |
| more than CNY 5000 | 48 | 13.15% | 100% | 11.20% | 100% |
| add up the total | 430 | 100% | - | 100% | - |
Questionnaire variable definitions.
| Serial Number | Variable Name | Definitional Approach and Scale |
|---|---|---|
| F1 | genders | 1 for male, 2 for female |
| F2 | age groups | 1 indicates 45–60 years old, 2 indicates 60–75 years old, 3 indicates 75–90 years old, and 4 indicates 90 years old or older. |
| F3 | educational attainment | 1 means none, 2 means elementary school, 3 means middle school, 4 means high school, 5 means college, 6 means bachelor’s degree and above |
| F4 | employment | 1 for active, 2 for freelance, 3 for laid off or unemployed, 4 for retired |
| F5 | monthly personal income | 1 indicates no source of income, 2 indicates less than CNY 1000, 3 indicates CNY 1000 to CNY 2500, 4 indicates CNY 2500 to CNY 5000, and 5 indicates more than CNY 5000. |
| F6 | length of residence | 1 indicates less than 5 years, 2 indicates 5 to 10 years, 3 indicates 10 to 20 years, 4 indicates more than 20 years |
| F7 | number of elderly people | 1 for 0, 2 for 1, 3 for 2, 4 for more than 2 |
| F8 | retirement area selection | 1 for aging in place, 2 for aging in community, 3 for aging in institution |
| F9 | condition | 1 indicates total self-care, 2 indicates partial self-care, 3 indicates total lack of self-care |
| F10 | perceived usefulness | 1 indicates strong disagreement, 2 indicates less agreement, 3 indicates uncertainty, 4 indicates more agreement, 5 indicates complete agreement |
| F11 | perceived ease of use | |
| F12 | use of attitudes (perceived behavioral control) | |
| F13 | subjective norm | |
| F14 | perceived price (attitude toward behavior) | |
| F15 | perceived effectiveness benefits | |
| F16 | perceived psychological benefits | |
| F17 | perceived social benefits | |
| F18 | perceived economic risk | |
| F19 | perceived technological risk | |
| F20 | perceived situational risk | |
| WTR | willingness to retrofit | 1 means strongly disagree, 2 means not quite agree, 3 means not sure, 4 means more agree, 5 means completely agree |
| WTP | willingness to pay | 1 indicates very reluctant, 2 indicates less reluctant, 3 indicates uncertain, 4 indicates more reluctant, 5 indicates completely reluctant |
| RB | retrofit budget | 1 indicates unwillingness to pay, 2 indicates less than CNY 1000, 3 indicates CNY 1000 to CNY 3000, 4 indicates CNY 3000 to CNY 5000, and 6 indicates more than CNY 5000 |
Pearson correlation analysis 1.
| F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | WTR | WTP | RA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F1 | 1 | |||||||||||
| F2 | −0.087 | 1 | ||||||||||
| 0.335 | ||||||||||||
| F3 | 0.127 | −0.589 ** | 1 | |||||||||
| 0.159 | 0.000 | |||||||||||
| F4 | −0.155 | 0.614 ** | −0.570 ** | 1 | ||||||||
| 0.084 | 0.000 | 0.000 | ||||||||||
| F5 | 0.129 | −0.183 * | 0.361 ** | −0.414 ** | 1 | |||||||
| 0.152 | 0.041 | 0.000 | 0.000 | |||||||||
| F6 | −0.155 | 0.343 ** | −0.324 ** | 0.224 * | −0.072 | 1 | ||||||
| 0.085 | 0.000 | 0.000 | 0.012 | 0.423 | ||||||||
| F7 | −0.055 | −0.143 | 0.170 | −0.140 | 0.178 * | −0.011 | 1 | |||||
| 0.542 | 0.112 | 0.058 | 0.120 | 0.047 | 0.899 | |||||||
| F8 | 0.051 | 0.174 | 0.090 | −0.040 | 0.099 | −0.268 ** | 0.091 | 1 | ||||
| 0.569 | 0.052 | 0.316 | 0.657 | 0.272 | 0.002 | 0.315 | ||||||
| F9 | −0.014 | 0.499 ** | −0.356 ** | 0.313 ** | −0.122 | 0.164 | −0.100 | 0.308 ** | 1 | |||
| 0.878 | 0.000 | 0.000 | 0.000 | 0.174 | 0.068 | 0.267 | 0.000 | |||||
| WTR | 0.076 | −0.007 | 0.094 | −0.036 | −0.074 | −0.092 | 0.041 | −0.195 * | 0.057 | 1 | ||
| 0.398 | 0.937 | 0.297 | 0.688 | 0.410 | 0.307 | 0.653 | 0.029 | 0.530 | ||||
| WTP | 0.008 | −0.190 * | 0.182 * | −0.145 | −0.069 | −0.168 | 0.047 | −0.214 * | −0.041 | 0.483 ** | 1 | |
| 0.926 | 0.034 | 0.042 | 0.107 | 0.442 | 0.060 | 0.603 | 0.016 | 0.646 | 0.000 | |||
| RA | 0.002 | −0.281 ** | 0.392 ** | −0.408 ** | 0.175 | −0.249 ** | 0.102 | −0.244 ** | −0.061 | 0.297 ** | 0.555 ** | 1 |
| 0.986 | 0.001 | 0.000 | 0.000 | 0.050 | 0.005 | 0.256 | 0.006 | 0.496 | 0.001 | 0.000 |
*. Significant at the 0.05 level (two-tailed). **. Significant correlation at the 0.01 level (two-tailed).
Pearson correlation analysis 2.
| F10 | F11 | F12 | F13 | F14 | F15 | F16 | F17 | F18 | F19 | F20 | WTR | WTP | RA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F10 | 1 | |||||||||||||
| F11 | 0.064 | 1 | ||||||||||||
| 0.482 | ||||||||||||||
| F12 | 0.031 | 0.763 ** | 1 | |||||||||||
| 0.731 | 0.000 | |||||||||||||
| F13 | 0.098 | 0.549 ** | 0.527 ** | 1 | ||||||||||
| 0.278 | 0.000 | 0.000 | ||||||||||||
| F14 | 0.578 ** | 0.223 * | 0.134 | 0.288 ** | 1 | |||||||||
| 0.000 | 0.012 | 0.137 | 0.001 | |||||||||||
| F15 | 0.681 ** | 0.082 | 0.020 | 0.199 * | 0.723 ** | 1 | ||||||||
| 0.000 | 0.362 | 0.827 | 0.026 | 0.000 | ||||||||||
| F16 | 0.710 ** | 0.062 | −0.082 | 0.107 | 0.661 ** | 0.814 ** | 1 | |||||||
| 0.000 | 0.494 | 0.364 | 0.237 | 0.000 | 0.000 | |||||||||
| F17 | 0.684 ** | 0.162 | 0.048 | 0.257 ** | 0.667 ** | 0.819 ** | 0.822 ** | 1 | ||||||
| 0.000 | 0.070 | 0.598 | 0.004 | 0.000 | 0.000 | 0.000 | ||||||||
| F18 | 0.038 | 0.381 ** | 0.430 ** | 0.624 ** | 0.249 ** | 0.134 | 0.096 | 0.166 | 1 | |||||
| 0.673 | 0.000 | 0.000 | 0.000 | 0.005 | 0.137 | 0.285 | 0.064 | |||||||
| F19 | −0.121 | 0.467 ** | 0.546 ** | 0.639 ** | 0.090 | −0.016 | −0.072 | 0.053 | 0.727 ** | 1 | ||||
| 0.179 | 0.000 | 0.000 | 0.000 | 0.319 | 0.858 | 0.427 | 0.560 | 0.000 | ||||||
| F20 | −0.019 | 0.445 ** | 0.559 ** | 0.548 ** | 0.116 | 0.031 | 0.002 | 0.097 | 0.638 ** | 0.790 ** | 1 | |||
| 0.836 | 0.000 | 0.000 | 0.000 | 0.196 | 0.734 | 0.982 | 0.283 | 0.000 | 0.000 | |||||
| WTR | 0.159 | 0.013 | 0.014 | 0.172 | 0.089 | 0.229 * | 0.167 | 0.225 * | 0.104 | 0.064 | 0.026 | 1 | ||
| 0.076 | 0.883 | 0.879 | 0.055 | 0.325 | 0.010 | 0.063 | 0.012 | 0.247 | 0.476 | 0.778 | ||||
| WTP | 0.032 | −0.183 * | 0.007 | 0.137 | −0.071 | 0.034 | 0.019 | 0.102 | 0.057 | 0.076 | 0.012 | 0.483 ** | 1 | |
| 0.721 | 0.041 | 0.937 | 0.128 | 0.429 | 0.706 | 0.838 | 0.259 | 0.530 | 0.398 | 0.894 | 0.000 | |||
| RA | −0.112 | 0.162 | 0.080 | 0.195 * | −0.118 | −0.112 | −0.149 | −0.035 | 0.038 | 0.051 | −0.041 | 0.297 ** | 0.555 ** | 1 |
| 0.215 | 0.070 | 0.377 | 0.030 | 0.188 | 0.212 | 0.097 | 0.697 | 0.675 | 0.573 | 0.648 | 0.001 | 0.000 |
*. Significant at the 0.05 level (two-tailed). **. Significant correlation at the 0.01 level (two-tailed).
Analysis of variance table of willingness to retrofit and influencing factors.
| Square Sum | Degrees of Freedom | Mean Square | F | Significance | |
|---|---|---|---|---|---|
| genders | 0.536 | 4 | 0.134 | 0.526 | 0.717 |
| age groups | 1.751 | 4 | 0.438 | 0.376 | 0.825 |
| educational attainment | 14.411 | 4 | 3.603 | 1.537 | 0.196 |
| employment | 12.382 | 4 | 3.095 | 2.002 | 0.098 |
| monthly personal income | 5.445 | 4 | 1.361 | 1.068 | 0.376 |
| length of residence | 2.969 | 4 | 0.742 | 0.743 | 0.565 |
| number of elderly people | 5.038 | 4 | 1.259 | 1.444 | 0.224 |
| retirement area selection | 1.205 | 4 | 0.301 | 1.379 | 0.245 |
| condition | 0.509 | 4 | 0.127 | 0.430 | 0.787 |
| perceived usefulness | 3.127 | 4 | 0.782 | 1.533 | 0.197 |
| perceived ease of use | 4.309 | 4 | 1.077 | 0.805 | 0.524 |
| use of attitudes | 3.782 | 4 | 0.945 | 0.759 | 0.554 |
| subjective norm | 15.196 | 4 | 3.799 | 2.570 | 0.041 |
| perceived price | 3.165 | 4 | 0.791 | 1.330 | 0.263 |
| perceived effectiveness benefits | 4.011 | 4 | 1.003 | 2.616 | 0.039 |
| perceived psychological benefits | 2.697 | 4 | 0.674 | 1.667 | 0.162 |
| perceived social benefits | 4.026 | 4 | 1.006 | 2.852 | 0.027 |
| perceived economic risk | 6.585 | 4 | 1.646 | 1.381 | 0.245 |
| perceived technology risk | 5.128 | 4 | 1.282 | 0.907 | 0.462 |
| perceived situational risk | 5.207 | 4 | 1.302 | 0.904 | 0.464 |
Analysis of variance table of WTP and influencing factors.
| Square Sum | Degrees of Freedom | Mean Square | F | Significance | |
|---|---|---|---|---|---|
| genders | 0.257 | 4 | 0.064 | 0.249 | 0.909 |
| age groups | 7.804 | 4 | 1.951 | 1.751 | 0.143 |
| educational attainment | 33.163 | 4 | 8.291 | 3.789 | 0.006 |
| employment | 6.521 | 4 | 1.630 | 1.022 | 0.399 |
| monthly personal income | 2.904 | 4 | 0.726 | 0.560 | 0.692 |
| length of residence | 5.023 | 4 | 1.256 | 1.278 | 0.282 |
| number of elderly people | 1.559 | 4 | 0.390 | 0.432 | 0.785 |
| retirement area selection | 1.379 | 4 | 0.345 | 1.589 | 0.182 |
| condition | 0.253 | 4 | 0.063 | 0.212 | 0.931 |
| perceived usefulness | 4.742 | 4 | 1.185 | 2.388 | 0.055 |
| perceived ease of use | 6.875 | 4 | 1.719 | 1.305 | 0.272 |
| use of attitudes | 1.161 | 4 | 0.290 | 0.229 | 0.922 |
| subjective norm | 6.639 | 4 | 1.660 | 1.071 | 0.374 |
| perceived price | 2.224 | 4 | 0.556 | 0.923 | 0.453 |
| perceived effectiveness benefits | 3.307 | 4 | 0.827 | 2.124 | 0.082 |
| perceived psychological benefits | 4.224 | 4 | 1.056 | 2.696 | 0.034 |
| perceived social benefits | 4.218 | 4 | 1.054 | 3.002 | 0.021 |
| perceived economic risk | 2.206 | 4 | 0.552 | 0.449 | 0.773 |
| perceived technology risk | 4.362 | 4 | 1.091 | 0.768 | 0.548 |
| perceived situational risk | 0.185 | 4 | 0.046 | 0.031 | 0.998 |
Analysis of variance table of retrofit budget and influencing factors.
| Square Sum | Degrees of Freedom | Mean Square | F | Significance | |
|---|---|---|---|---|---|
| genders | 0.191 | 4 | 0.048 | 0.185 | 0.946 |
| age groups | 11.989 | 4 | 2.997 | 2.777 | 0.030 |
| educational attainment | 46.964 | 4 | 11.741 | 5.664 | 0.000 |
| employment | 47.046 | 4 | 11.762 | 9.357 | 0.000 |
| monthly personal income | 17.951 | 4 | 4.488 | 3.833 | 0.006 |
| length of residence | 8.559 | 4 | 2.140 | 2.245 | 0.068 |
| number of elderly people | 3.053 | 4 | 0.763 | 0.859 | 0.491 |
| retirement area selection | 2.712 | 4 | 0.678 | 3.295 | 0.013 |
| condition | 0.582 | 4 | 0.145 | 0.492 | 0.742 |
| perceived usefulness | 1.204 | 4 | 0.301 | 0.573 | 0.683 |
| perceived ease of use | 9.998 | 4 | 2.500 | 1.936 | 0.109 |
| use of attitudes | 6.449 | 4 | 1.612 | 1.318 | 0.267 |
| subjective norm | 12.155 | 4 | 3.039 | 2.021 | 0.096 |
| perceived price | 2.007 | 4 | 0.502 | 0.830 | 0.508 |
| perceived effectiveness benefits | 1.443 | 4 | 0.361 | 0.891 | 0.472 |
| perceived psychological benefits | 2.416 | 4 | 0.604 | 1.485 | 0.211 |
| perceived social benefits | 0.696 | 4 | 0.174 | 0.457 | 0.767 |
| perceived economic risk | 3.954 | 4 | 0.988 | 0.814 | 0.518 |
| perceived technology risk | 6.592 | 4 | 1.648 | 1.176 | 0.325 |
| perceived situational risk | 18.346 | 4 | 4.586 | 3.447 | 0.011 |
Mean scores on questionnaire scales.
| Variant | Scale Issues | Score | |
|---|---|---|---|
| Perceived usefulness | I think remodeling is the future. | 4.48 | 4.50 |
| I think remodeling can be beneficial to non-elderly residents as well | 4.52 | ||
| Perceived ease of use | I think the remodeling will be somewhat hindered | 4.05 | |
| Use of attitudes | I think remodeling is more difficult | 4.01 | |
| Subjective norm | I believe that the advice of others influences the decisions I make | 3.97 | 4.08 |
| I believe that government policy influences my decision-making | 4.19 | ||
| Perceived price | I think there is value in making modifications | 4.50 | |
| Perceived effectiveness benefits | I think Habitat is better after the renovation | 4.53 | 4.555 |
| I think it is safer and more comfortable for seniors to live after remodeling | 4.58 | ||
| Perceived Psychological benefits | I think remodeling is good for older people’s mental health | 4.56 | 4.56 |
| Perceived social benefits | I think the remodeling is conducive to a good social climate | 4.57 | 4.535 |
| I think remodeling is the embodiment of filial piety to elders | 4.50 | ||
| Perceived economic risk | I think the economic cost of retrofitting is higher | 4.17 | |
| Perceived technological risk | I do not think the remodeling technology is mature enough | 3.94 | |
| Perceived situational risk | I do not think the remodeling is meeting expectations | 3.92 | |
Characteristics of people with high willingness to pay.
| Investigative Projects | Disaggregated Indicators | Sample Size | Percentage |
|---|---|---|---|
| genders | male | 23 | 47.9% |
| female | 25 | 52.1% | |
| age | 45~60 years | 33 | 68.8% |
| 60~75 years | 7 | 14.6% | |
| 75–90 years | 6 | 12.5% | |
| 90 years and over | 2 | 4.2% | |
| educational attainment | not | 1 | 2.1% |
| elementary school | 11 | 22.9% | |
| junior high school | 4 | 8.3% | |
| senior high school | 12 | 25.0% | |
| junior college | 15 | 31.3% | |
| undergraduate and above | 5 | 10.4% | |
| employment | incumbency | 22 | 45.8% |
| profession | 13 | 27.1% | |
| laid off or unemployed | 1 | 2.1% | |
| retirement | 12 | 25.0% | |
| monthly personal income | less than CNY 1,00 | 2 | 4.2% |
| CNY 1000~2500 | 6 | 12.5% | |
| CNY 2500~5000 | 23 | 47.9% | |
| more than CNY 5000 | 16 | 33.3% | |
| no source of income | 1 | 2.1% | |
| length of residence | less than 5 years | 5 | 10.4% |
| 5~10 years | 15 | 31.3% | |
| 10–20 years | 13 | 27.1% | |
| more than 20 years | 15 | 31.3% | |
| number of elderly people | 0 | 11 | 22.9% |
| 1 | 15 | 31.3% | |
| 2 | 13 | 27.1% | |
| 2 or more | 9 | 18.8% | |
| retirement area selection | living at home in one’s old age | 40 | 83.3% |
| community care | 5 | 10.4% | |
| care organization | 3 | 6.3% | |
| condition | fully self-managed | 40 | 83.3% |
| partially self-care | 8 | 16.7% | |
| completely incapable of self-care. | 0 | 0% |
References
1. Hanmo, Y. Dynamic Trend of China’s Population Ageing and New Characteristics of the Elderly. Popul. Res.; 2022; 46, 104.
2. Liu, C.; Zhou, S.; Bai, X. Ageing in China: Trends, challenges and opportunities. Aging Across Cultures: Growing Old in the Non-Western World; Springer: Cham, Switzerland, 2021; pp. 137-152.
3. Bai, C.; Lei, X. New trends in population aging and challenges for China’s sustainable development. China Econ. J.; 2020; 13, pp. 3-23. [DOI: https://dx.doi.org/10.1080/17538963.2019.1700608]
4. Kang, J.; Wu, F. The Impact of Steady Flow of Rural Labor Force in China on Rural Aging: Empirical Analysis Based on the Data from 2009–2021 Thousand-village Survey. J. Financ. Econ.; 2024; 50, pp. 78-92.
5. Zeng, Y.; Wang, Z. A policy analysis on challenges and opportunities of population/household aging in China. J. Popul. Ageing; 2014; 7, pp. 255-281. [DOI: https://dx.doi.org/10.1007/s12062-014-9102-y]
6. Liu, Y.; Long, H.; Chen, Y.; Wang, J.; Li, Y.; Li, Y.; Zhou, Y. Progress of research on urban-rural transformation and rural development in China in the past decade and future prospects. J. Geogr. Sci.; 2016; 26, pp. 1117-1132. [DOI: https://dx.doi.org/10.1007/s11442-016-1318-8]
7. Han, J. Prioritizing agricultural, rural development and implementing the rural revitalization strategy. China Agric. Econ. Rev.; 2020; 12, pp. 14-19. [DOI: https://dx.doi.org/10.1108/CAER-02-2019-0026]
8. Xie, L. Environmental governance and public participation in rural China. China Inf.; 2016; 30, pp. 188-208. [DOI: https://dx.doi.org/10.1177/0920203X16653880]
9. Liu, J. Ageing in rural China: Migration and care circulation. J. Chin. Sociol.; 2016; 3, 9. [DOI: https://dx.doi.org/10.1186/s40711-016-0030-5]
10. Liu, Y.; He, S.; Wu, F.; Webster, C. Urban villages under China’s rapid urbanization: Unregulated assets and transitional neighbourhoods. Habitat Int.; 2010; 34, pp. 135-144. [DOI: https://dx.doi.org/10.1016/j.habitatint.2009.08.003]
11. Tian, L.; Ge, B.; Li, Y. Impacts of state-led and bottom-up urbanization on land use change in the peri-urban areas of Shanghai: Planned growth or uncontrolled sprawl?. Cities; 2017; 60, pp. 476-486. [DOI: https://dx.doi.org/10.1016/j.cities.2016.01.002]
12. Zheng, X.; Liu, L.; Pang, L.; Qiu, Y.; Yang, C.; Chen, Q.; Li, Q. Effects of rapid economic development on traditional patterns of elder support in China. J. Popul. Ageing; 2012; 5, pp. 163-176. [DOI: https://dx.doi.org/10.1007/s12062-012-9065-9]
13. Wang, D. China’s urban and rural old age security system: Challenges and options. China World Econ.; 2006; 14, pp. 102-116. [DOI: https://dx.doi.org/10.1111/j.1749-124X.2006.00001.x]
14. Peng, C.; Burr, J.A.; Han, S.H. Cognitive function and cognitive decline among older rural Chinese adults: The roles of social support, pension benefits, and medical insurance. Aging Ment. Health; 2023; 27, pp. 771-779. [DOI: https://dx.doi.org/10.1080/13607863.2022.2088693]
15. Romé, Å.; Persson, U.; Ekdahl, C.; Gard, G. Willingness to pay for health improvements of physical activity on prescription. Scand. J. Public Health; 2010; 38, pp. 151-159. [DOI: https://dx.doi.org/10.1177/1403494809357099]
16. Bai, C.; Zhan, J.; Wang, H.; Yang, Z.; Liu, H.; Liu, W.; Teng, Y. Heating choices and residential willingness to pay for clean heating: Evidence from a household survey in rural China. Energy Policy; 2023; 178, 113617. [DOI: https://dx.doi.org/10.1016/j.enpol.2023.113617]
17. Wang, H.; Mullahy, J. Willingness to pay for reducing fatal risk by improving air quality: A contingent valuation study in Chongqing, China. Sci. Total Environ.; 2006; 367, pp. 50-57. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2006.02.049]
18. Halkos, G.; Matsiori, S. Determinants of willingness to pay for coastal zone quality improvement. J. Socio-Econ.; 2012; 41, pp. 391-399. [DOI: https://dx.doi.org/10.1016/j.socec.2012.04.010]
19. Ellison, C.; Struckmeyer, L.; Kazem-Zadeh, M.; Campbell, N.; Ahrentzen, S.; Classen, S. A social-ecological approach to identify facilitators and barriers of home modifications. Int. J. Environ. Res. Public Health; 2021; 18, 8720. [DOI: https://dx.doi.org/10.3390/ijerph18168720] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34444467]
20. Sheth, S.; Cogle, C.R. Home modifications for older adults: A systematic review. J. Appl. Gerontol.; 2023; 42, pp. 1151-1164. [DOI: https://dx.doi.org/10.1177/07334648231151669]
21. Cook, C.C.; Yearns, M.H.; Martin, P. Aging in place: Home modifications among rural and urban elderly. Hous. Soc.; 2005; 32, pp. 85-106. [DOI: https://dx.doi.org/10.1080/08882746.2005.11430515]
22. Gao, Y.; Wu, J. Aging in place in rural Northeast China: A mixed methods examination of the influence of social capital on mental well-being in middle and late adulthood. Front. Public Health; 2024; 11, 1261132. [DOI: https://dx.doi.org/10.3389/fpubh.2023.1261132] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38249362]
23. Pho, A.T.; Tanner, E.K.; Roth, J.; Greeley, M.E.; Dorsey, C.D.; Szanton, S.L. Nursing strategies for promoting and maintaining function among community-living older adults: The CAPABLE intervention. Geriatr. Nurs.; 2012; 33, pp. 439-445. [DOI: https://dx.doi.org/10.1016/j.gerinurse.2012.04.002] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22651978]
24. Stark, S.; Landsbaum, A.; Palmer, J.L.; Somerville, E.K.; Morris, J.C.; Harvey, A.; Friedman, D.H. Client-centred home modifications improve daily activity performance of older adults. Can. J. Occup. Ther.; 2009; 76, (Suppl. S1), pp. 235-245. [DOI: https://dx.doi.org/10.1177/000841740907600s09] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19757729]
25. Riazi, A.; Ying Boon, M.; Bridge, C.; Dain, S.J. Home modification guidelines as recommended by visually impaired people. J. Assist. Technol.; 2012; 6, pp. 270-284. [DOI: https://dx.doi.org/10.1108/17549451211285762]
26. Keall, M.D.; Tupara, H.; Pierse, N.; Wilkie, M.; Baker, M.G.; Howden-Chapman, P.; Cunningham, C. Home modifications to prevent home fall injuries in houses with Māori occupants (MHIPI): A randomised controlled trial. Lancet Public Health; 2021; 6, pp. e631-e640. [DOI: https://dx.doi.org/10.1016/S2468-2667(21)00135-3]
27. Brody, A.A.; Guan, C.; Cortes, T.; Galvin, J.E. Development and testing of the Dementia Symptom Management at Home (DSM-H) program: An interprofessional home health care intervention to improve the quality of life for persons with dementia and their caregivers. Geriatr. Nurs.; 2016; 37, pp. 200-206. [DOI: https://dx.doi.org/10.1016/j.gerinurse.2016.01.002]
28. Gadakari, T.; Wang, J.; Hadjri, K.; Huang, J. Promoting Ageing-in-Place: Design of residential buildings for older people in China. Environ.-Behav. Proc. J.; 2017; 2, pp. 113-121. [DOI: https://dx.doi.org/10.21834/e-bpj.v2i6.965]
29. Zhang, L.; Zeng, Y.; Wang, L.; Fang, Y. Urban–rural differences in long-term care service status and needs among home-based elderly people in China. Int. J. Environ. Res. Public Health; 2020; 17, 1701. [DOI: https://dx.doi.org/10.3390/ijerph17051701]
30. Zhang, J.; Shanat, M.B.H. Research on Design and Development of Elder-Friendly Furniture in Chinese Residential Situation. Art Soc.; 2023; 2, pp. 32-37. [DOI: https://dx.doi.org/10.56397/AS.2023.06.04]
31. Liu, Y.; Li, H.; Li, W.; Wang, S. Renovation priorities for old residential districts based on resident satisfaction: An application of asymmetric impact-performance analysis in Xi’an, China. PLoS ONE; 2021; 16, e0254372. [DOI: https://dx.doi.org/10.1371/journal.pone.0254372]
32. Zhuo, X.; Li, H. A study on cost allocation in renovation of old urban residential communities. Sustainability; 2022; 14, 6929. [DOI: https://dx.doi.org/10.3390/su14116929]
33. Zhou, H.; Wang, A. Research on the Universal Design of Rural Public Spaces in the Context of an Aging Society. Adv. Educ. Humanit. Soc. Sci. Res.; 2023; 7, 105. [DOI: https://dx.doi.org/10.56028/aehssr.7.1.105.2023]
34. Banfi, S.; Farsi, M.; Filippini, M.; Jakob, M. Willingness to pay for energy-saving measures in residential buildings. Energy Econ.; 2008; 30, pp. 503-516. [DOI: https://dx.doi.org/10.1016/j.eneco.2006.06.001]
35. Collins, M.; Curtis, J. Willingness-to-pay and free-riding in a national energy efficiency retrofit grant scheme. Energy Policy; 2018; 118, pp. 211-220. [DOI: https://dx.doi.org/10.1016/j.enpol.2018.03.057]
36. Huang, C.; Ma, J.; Song, K. Homeowners’ willingness to make investment in energy efficiency retrofit of residential buildings in China and its influencing factors. Energies; 2021; 14, 1260. [DOI: https://dx.doi.org/10.3390/en14051260]
37. Jia, J.J.; Wu, H.Q.; Nie, H.G.; Fan, Y. Modeling the willingness to pay for energy efficient residence in urban residential sector in China. Energy Policy; 2019; 135, 111003. [DOI: https://dx.doi.org/10.1016/j.enpol.2019.111003]
38. Streimikiene, D.; Balezentis, T. Willingness to pay for renovation of multi-flat buildings and to share the costs of renovation. Energies; 2020; 13, 2721. [DOI: https://dx.doi.org/10.3390/en13112721]
39. März, S.; Stelk, I.; Stelzer, F. Are tenants willing to pay for energy efficiency? Evidence from a small-scale spatial analysis in Germany. Energy Policy; 2022; 161, 112753. [DOI: https://dx.doi.org/10.1016/j.enpol.2021.112753]
40. Streicher, K.N.; Mennel, S.; Chambers, J.; Parra, D.; Patel, M.K. Cost-effectiveness of large-scale deep energy retrofit packages for residential buildings under different economic assessment approaches. Energy Build.; 2020; 215, 109870. [DOI: https://dx.doi.org/10.1016/j.enbuild.2020.109870]
41. Amstalden, R.W.; Kost, M.; Nathani, C.; Imboden, D.M. Economic potential of energy-efficient retrofitting in the Swiss residential building sector: The effects of policy instruments and energy price expectations. Energy Policy; 2007; 35, pp. 1819-1829. [DOI: https://dx.doi.org/10.1016/j.enpol.2006.05.018]
42. Portnov, B.A.; Trop, T.; Svechkina, A.; Ofek, S.; Akron, S.; Ghermandi, A. Factors affecting homebuyers’ willingness to pay green building price premium: Evidence from a nationwide survey in Israel. Build. Environ.; 2018; 137, pp. 280-291. [DOI: https://dx.doi.org/10.1016/j.buildenv.2018.04.014]
43. Zheng, D.; Yu, L.; Wang, L.; Tao, J. Integrating willingness analysis into investment prediction model for large scale building energy saving retrofit: Using fuzzy multiple attribute decision making method with Monte Carlo simulation. Sustain. Cities Soc.; 2019; 44, pp. 291-309. [DOI: https://dx.doi.org/10.1016/j.scs.2018.10.008]
44. Li, S.; Kallas, Z. Meta-analysis of consumers’ willingness to pay for sustainable food products. Appetite; 2021; 163, 105239. [DOI: https://dx.doi.org/10.1016/j.appet.2021.105239]
45. Godin, G.; Kok, G. The theory of planned behavior: A review of its applications to health-related behaviors. Am. J. Health Promot.; 1996; 11, pp. 87-98. [DOI: https://dx.doi.org/10.4278/0890-1171-11.2.87] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10163601]
46. Zauner, A.; Koller, M.; Hatak, I. Customer perceived value—Conceptualization and avenues for future research. Cogent Psychol.; 2015; 2, 1061782. [DOI: https://dx.doi.org/10.1080/23311908.2015.1061782]
47. Crowe, M.D.; Schuh, S.D.; Stavins, J. Consumer Behavior and Payment Choice: A Conference Summary; Federal Reserve Bank of Boston: Boston, MA, USA, 2007.
48. Mahadeo, J.D. Towards an Understanding of the Factors Influencing the Acceptance and Diffusion of e-Government Services. Electron. J. E-Gov.; 2009; 7, pp. 381-392.
49. Zhang, L.; Chen, L.; Wu, Z.; Xue, H.; Dong, W. Key factors affecting informed consumers’ willingness to pay for green housing: A case study of Jinan, China. Sustainability; 2018; 10, 1711. [DOI: https://dx.doi.org/10.3390/su10061711]
50. Zhang, L.; Chen, L.; Wu, Z.; Zhang, S.; Song, H. Investigating young consumers’ purchasing intention of green housing in China. Sustainability; 2018; 10, 1044. [DOI: https://dx.doi.org/10.3390/su10041044]
51. Tan, W.L.; Goh, Y.N. The role of psychological factors in influencing consumer purchase intention towards green residential building. Int. J. Hous. Mark. Anal.; 2018; 11, pp. 788-807. [DOI: https://dx.doi.org/10.1108/IJHMA-11-2017-0097]
52. Bai, X.N.; Jung, G.Y.; Kim, H.H. A Study on the Purchase Intention of Ginseng Products based on the theory of Perceived Value and TPB. J. Korea Converg. Soc.; 2019; 10, pp. 35-42.
53. Lim, Y.J.; Osman, A.; Salahuddin, S.N.; Romle, A.R.; Abdullah, S. Factors influencing online shopping behavior: The mediating role of purchase intention. Procedia Econ. Financ.; 2016; 35, pp. 401-410. [DOI: https://dx.doi.org/10.1016/S2212-5671(16)00050-2]
54. Gu, S.; Wu, Y. Using the Theory of Planned Behaviour to explain customers’ online purchase intention. World Sci. Res. J.; 2019; 5, pp. 226-249.
55. Ketabi, S.N.; Ranjbarian, B.; Ansari, A. Analysis of the effective factors on online purchase intention through theory of planned behavior. Int. J. Acad. Res. Bus. Soc. Sci.; 2014; 4, pp. 374-382. [DOI: https://dx.doi.org/10.6007/IJARBSS/v4-i4/808]
56. Wang, T.; Liu, L.; Huang, S. Study on the Influence of Policy Guidance and Market-Driven Factors on Farmers’ Behavior Regarding Black Soil Protection. Land; 2024; 13, 1082. [DOI: https://dx.doi.org/10.3390/land13071082]
57. Lim, W.M.; Yong, J.L.S.; Suryadi, K. Consumers’ perceived value and willingness to purchase organic food. J. Glob. Mark.; 2014; 27, pp. 298-307. [DOI: https://dx.doi.org/10.1080/08911762.2014.931501]
58. Rodríguez-Ortega, T.; Bernués, A.; Alfnes, F. Psychographic profile affects willingness to pay for ecosystem services provided by Mediterranean high nature value farmland. Ecol. Econ.; 2016; 128, pp. 232-245. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2016.05.002]
59. Swait, J.; Sweeney, J.C. Perceived value and its impact on choice behavior in a retail setting. J. Retail. Consum. Serv.; 2020; 7, pp. 77-88. [DOI: https://dx.doi.org/10.1016/S0969-6989(99)00012-0]
60. Li, Y.; Zhou, T.; Wang, Z.; Li, W.; Zhou, L.; Cao, Y.; Shen, Q. Environment improvement and energy saving in Chinese rural housing based on the field study of thermal adaptability. Energy Sustain. Dev.; 2022; 71, pp. 315-329. [DOI: https://dx.doi.org/10.1016/j.esd.2022.10.006]
61. Xu, S.; Zhang, T.; Fukuda, H.; He, J.; Bao, X. Comprehensive Study of Residential Environment Preferences and Characteristics among Older Adults: Empirical Evidence from China. Buildings; 2024; 14, 2175. [DOI: https://dx.doi.org/10.3390/buildings14072175]
62. Huang, G.; Duan, Y.; Guo, F.; Chen, G. Prevalence and related influencing factors of depression symptoms among empty-nest older adults in China. Arch. Gerontol. Geriatr.; 2020; 91, 104183. [DOI: https://dx.doi.org/10.1016/j.archger.2020.104183]
63. Liu, L.J.; Guo, Q. Loneliness and health-related quality of life for the empty nest elderly in the rural area of a mountainous county in China. Qual. Life Res.; 2007; 16, pp. 1275-1280. [DOI: https://dx.doi.org/10.1007/s11136-007-9250-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17703375]
64. Bacsu, J.; Jeffery, B.; Abonyi, S.; Johnson, S.; Novik, N.; Martz, D.; Oosman, S. Healthy aging in place: Perceptions of rural older adults. Educ. Gerontol.; 2014; 40, pp. 327-337. [DOI: https://dx.doi.org/10.1080/03601277.2013.802191]
65. Kaup, M.; Richardson, H.; Adkins, M.; LaFleur, B.; Tucker, S.; Tines, L. Low-Income Cooperative Living Environments: An Analysis of Universal Design Features That Support Aging in Place. Innov. Aging; 2020; 4, (Suppl. S1), 435. [DOI: https://dx.doi.org/10.1093/geroni/igaa057.1405]
66. Lee, H.; Park, S.J.; Kim, M.J.; Jung, J.Y.; Lim, H.W.; Kim, J.T. The service pattern-oriented smart bedroom based on elderly spatial behaviour patterns. Indoor Built Environ.; 2013; 22, pp. 299-308. [DOI: https://dx.doi.org/10.1177/1420326X12469712]
67. Ma, C.; Guerra-Santin, O.; Mohammadi, M. Smart home modification design strategies for ageing in place: A systematic review. J. Hous. Built Environ.; 2022; 37, pp. 625-651. [DOI: https://dx.doi.org/10.1007/s10901-021-09888-z]
68. Song, J. Study on the Rural Aging Adaptation Program under the Model of Aging in Place--The Case of Houkuang Village in Shandong Province. Adv. Eng. Technol. Res.; 2024; 10, 315. [DOI: https://dx.doi.org/10.56028/aetr.10.1.315.2024]
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Lai, Xuanying 1 1 Business School, Hohai University, Nanjing 210000, China;
2 Migrant Workers Research Center in Anhui, Fuyang Normal University, Fuyang 236000, China;